Utilization Trend Engine

python
# Utilization Trend Analysis
utilization_categories = {
    'emergency_room': {
        'visits_pmpy': lambda claims: calculate_pmpy(claims, place_of_service='23'),
        'benchmark': 2.8,
        'target_range': (1.8, 3.2)
    },
    'inpatient_admits': {
        'admits_pmpy': lambda claims: calculate_pmpy(claims, revenue_code='0100-0219'),
        'benchmark': 0.065,
        'target_range': (0.050, 0.080)
    },
    'mri_ct_scans': {
        'scans_pmpy': lambda claims: calculate_pmpy(claims, cpt_prefix=['70', '71', '72', '73']),
        'benchmark': 0.95,
        'target_range': (0.70, 1.20)
    },
    'preventive_colonoscopy': {
        'procedures_pmpy': lambda claims: calculate_pmpy(claims, cpt_code='G0121'),
        'benchmark': 0.082,  # Age 50+ eligible population
        'target_range': (0.070, 0.095)
    }
}

def analyze_utilization_trends(current_claims, prior_claims, members):
    results = {}
    
    for category, config in utilization_categories.items():
        # Current Period Utilization
        current_services = count_services(current_claims, config)
        current_member_months = members.current_period_member_months
        current_pmpy = (current_services / current_member_months) * 12
        
        # Prior Period Utilization
        prior_services = count_services(prior_claims, config)
        prior_member_months = members.prior_period_member_months
        prior_pmpy = (prior_services / prior_member_months) * 12
        
        # Trend Calculation
        utilization_trend = (current_pmpy / prior_pmpy) - 1
        
        # Benchmark Variance
        benchmark_variance = (current_pmpy / config['benchmark']) - 1
        
        # Flag Status
        if current_pmpy > config['target_range'][1]:
            flag = 'OVERUTILIZATION'
        elif current_pmpy < config['target_range'][0]:
            flag = 'UNDERUTILIZATION'
        else:
            flag = 'NORMAL'
        
        results[category] = {
            'current_pmpy': current_pmpy,
            'prior_pmpy': prior_pmpy,
            'utilization_trend': utilization_trend,
            'benchmark': config['benchmark'],
            'benchmark_variance': benchmark_variance,
            'flag': flag
        }
    
    return results

# Example Output:
# {
#   'emergency_room': {
#       'current_pmpy': 4.2,
#       'prior_pmpy': 3.8,
#       'utilization_trend': 0.105,  # +10.5%
#       'benchmark': 2.8,
#       'benchmark_variance': 0.50,  # 50% above benchmark
#       'flag': 'OVERUTILIZATION'
#   },
#   'preventive_colonoscopy': {
#       'current_pmpy': 0.058,
#       'prior_pmpy': 0.062,
#       'utilization_trend': -0.065,  # -6.5%
#       'benchmark': 0.082,
#       'benchmark_variance': -0.293,  # 29% below benchmark
#       'flag': 'UNDERUTILIZATION'
#   }
# }